Straight answers to the questions businesses actually ask

Your items, customers, suppliers, price lists, stock levels and open orders are extracted from the old system, cleaned up, and loaded into a test environment first. You validate that test data against reality — spot-check stock counts, run a few orders through — before anything goes live. Cut-over then happens at a chosen moment, typically a weekend or month-end, with the old system kept read-only as a fallback. Migration is also the best data clean-up opportunity you'll ever get: dead SKUs and duplicate customers don't have to come with you.

Yes — and it's where implementations succeed or fail. A system nobody uses correctly produces wrong stock numbers, and wrong stock numbers destroy trust in the whole project. Training is role-based: the warehouse team learns receiving and picking, the sales team learns orders and availability, the finance team learns how transactions flow to accounting. It happens on your data, in your test environment, before go-live, with support continuing afterwards while habits form.

Often, yes. Most "failed" implementations aren't failed software — they're systems that were configured around the wrong assumptions, migrated with dirty data, or handed over without training. A rescue starts with a diagnostic: what was the system supposed to do, where does reality diverge, and is the gap in configuration, data or adoption? Sometimes the fix is re-configuration and re-training on the system you already pay for; occasionally the honest answer is that it was the wrong system — and because we implement all four, we can say so.

It shouldn't, if it's planned properly. Almost all of the work — scoping, configuration, migration into a test environment, training — happens alongside your normal operations, not instead of them. The only genuinely sensitive moment is cut-over, which is scheduled for a quiet window such as a weekend or month-end, with the old system kept as a read-only fallback. Your team's biggest time commitment is validation and training sessions, which are scheduled around your operation.

AI-native software is custom software designed around AI capabilities from the ground up, rather than having AI features bolted onto a traditional system. In practice that means software that can read documents, interpret messy inputs, make judgement calls within rules you set, and handle the unstructured work — emails, PDFs, supplier price lists, freeform requests — that traditional systems force humans to re-type. It has become a realistic option for workflows that no off-the-shelf platform expresses well. More on AI-native custom solutions, and how it compares to traditional software.

It's held to the same security practices as any well-built custom software: access control so people only see what their role allows, encryption of data in transit and at rest, and private hosting arrangements so your business data isn't used to train public models. The AI component doesn't change the fundamentals — the questions to ask a builder are the same ones you'd ask about any system that touches your data, and a good builder will answer them plainly.

Yes — that's usually the point. Xero, Unleashed, Cin7, NetSuite and Odoo all expose APIs, and AI-native tools are typically built to sit alongside those systems rather than replace them: reading data out, writing clean transactions back, and handling the messy human-shaped work in between. A common pattern is an AI-native layer that turns unstructured inputs, like emailed orders or supplier documents, into structured records in the systems you already run.

When your workflow is the product. Off-the-shelf systems like Unleashed and Cin7 are excellent when your operation fits patterns they were designed for — and most operations do, which is why we implement them. Custom AI-native builds earn their keep when no platform expresses your workflow well, when you're paying people to bridge gaps between systems by re-typing and interpreting, or when the work involves documents and judgement calls that traditional software simply can't process. An independent partner who does both can tell you honestly which side of that line you're on — here's the evidence for why AI-native now.